Relative decline in density of Northern Hemisphere tree species in warm and arid regions of their climate niches.

Although climate change is expected to drive tree species toward colder and wetter regions of their distribution, broadscale empirical evidence is lacking. One possibility is that past and present human activities in forests obscure or alter the effects of climate. Here, using data from more than two million monitored trees from 73 widely distributed species, we quantify changes in tree species density within their climatic niches across Northern Hemisphere forests. We observe a reduction in mean density across species, coupled with a tendency toward increasing tree size. However, the direction and magnitude of changes in density exhibit considerable variability between species, influenced by stand development that results from previous stand-level disturbances. Remarkably, when accounting for stand development, our findings show a significant change in density toward cold and wet climatic conditions for 43% of the species, compared to only 14% of species significantly changing their density toward warm and arid conditions in both early- and late-development stands. The observed changes in climate-driven density showed no clear association with species traits related to drought tolerance, recruitment and dispersal capacity, or resource use, nor with the temperature or aridity affiliation of the species, leaving the underlying mechanism uncertain. Forest conservation policies and associated management strategies might want to consider anticipated long-term species range shifts alongside the integration of contemporary within-distribution density changes.

The anthropogenic imprint on temperate and boreal forest demography and carbon turnover.

Aim: The sweeping transformation of the biosphere by humans over the last millennia leaves only limited windows into its natural state. Much of the forests that dominated temperate and southern boreal regions have been lost and those that remain typically bear a strong imprint of forestry activities and past land-use change, which have changed forest age structure and composition. Here, we ask how would the dynamics, structure and function of temperate and boreal forests differ in the absence of forestry and the legacies of land-use change? Location: Global. Time Period: 2001-2014, integrating over the legacy of disturbance events from 1875 to 2014. Major Taxa Studied: Trees. Methods: We constructed an empirical model of natural disturbance probability as a function of community traits and climate, based on observed disturbance rate and form across 77 protected forest landscapes distributed across three continents. Coupling this within a dynamic vegetation model simulating forest composition and structure, we generated estimates of stand-replacing disturbance return intervals in the absence of forestry for northern hemisphere temperate and boreal forests. We then applied this model to calculate forest stand age structure and carbon turnover rates. Results: Comparison with observed disturbance rates revealed human activities to have almost halved the median return interval of stand-replacing disturbances across temperate forest, with more moderate changes in the boreal region. The resulting forests are typically much younger, especially in northern Europe and south-eastern North America, resulting in a 32% reduction in vegetation carbon turnover time across temperate forests and a 7% reduction for boreal forests. Conclusions: The current northern hemisphere temperate forest age structure is dramatically out of equilibrium with its natural disturbance regimes. Shifts towards more nature-based approaches to forest policy and management should more explicitly consider the current disturbance surplus, as it substantially impacts carbon dynamics and litter (including deadwood) stocks.

Forest demography and biomass accumulation rates are associated with transient mean tree size vs. density scaling relations.

Linking individual and stand-level dynamics during forest development reveals a scaling relationship between mean tree size and tree density in forest stands, which integrates forest structure and function. However, the nature of this so-called scaling law and its variation across broad spatial scales remain unquantified, and its linkage with forest demographic processes and carbon dynamics remains elusive. In this study, we develop a theoretical framework and compile a broad-scale dataset of long-term sample forest stands (n = 1,433) from largely undisturbed forests to examine the association of temporal mean tree size vs. density scaling trajectories (slopes) with biomass accumulation rates and the sensitivity of scaling slopes to environmental and demographic drivers. The results empirically demonstrate a large variation of scaling slopes, ranging from -4 to -0.2, across forest stands in tropical, temperate, and boreal forest biomes. Steeper scaling slopes are associated with higher rates of biomass accumulation, resulting from a lower offset of forest growth by biomass loss from mortality. In North America, scaling slopes are positively correlated with forest stand age and rainfall seasonality, thus suggesting a higher rate of biomass accumulation in younger forests with lower rainfall seasonality. These results demonstrate the strong association of the transient mean tree size vs. density scaling trajectories with forest demography and biomass accumulation rates, thus highlighting the potential of leveraging forest structure properties to predict forest demography, carbon fluxes, and dynamics at broad spatial scales.

Proton therapy for the management of localized prostate cancer: Long-term clinical outcomes at a comprehensive cancer center.

BACKGROUND AND PURPOSE: Proton therapy (PT) has emerged as a standard-of-care treatment option for localized prostate cancer at our comprehensive cancer center. However, there are few large-scale analyses examining the long-term clinical outcomes. Therefore, this article aims to evaluate the long-term effectiveness and toxicity of PT in patients with localized prostate cancer. MATERIALS AND METHODS: Review of 2772 patients treated from May 2006 through January 2020. Disease risk was stratified according to National Comprehensive Cancer Network guidelines as low [LR, n = 640]; favorable-intermediate [F-IR, n = 850]; unfavorable-intermediate [U-IR, n = 851]; high [HR, n = 315]; or very high [VHR, n = 116]. Biochemical failure and toxicity were analyzed using Kaplan-Meier estimates and multivariate models. RESULTS: The median patient age was 66 years; the median follow-up time was 7.0 years. Pelvic lymph node irradiation was prescribed to 28 patients (1%) (2 [0.2%] U-IR, 11 [3.5%] HR, and 15 [12.9%] VHR). The median dose was 78 Gy in 1.8-2.0 Gy(RBE) fractions. Freedom from biochemical relapse (FFBR) rates at 5 years and 10 years were 98.2% and 96.8% for the LR group; 98.3% and 93.6%, F-IR; 94.2% and 90.2%, U-IR; 94.3% and 85.2%, HR; and 86.1% and 68.5%, VHR. Two patients died of prostate cancer. Overall rates of late grade ≥ 3 GU and GI toxicity were 0.87% and 1.01%. CONCLUSIONS: Proton therapy for localized prostate cancer demonstrated excellent clinical outcomes in this large cohort, even among higher-risk groups with historically poor outcomes despite aggressive therapy.

A climate risk analysis of Earth's forests in the 21st century.

Earth's forests harbor extensive biodiversity and are currently a major carbon sink. Forest conservation and restoration can help mitigate climate change; however, climate change could fundamentally imperil forests in many regions and undermine their ability to provide such mitigation. The extent of climate risks facing forests has not been synthesized globally nor have different approaches to quantifying forest climate risks been systematically compared. We combine outputs from multiple mechanistic and empirical approaches to modeling carbon, biodiversity, and disturbance risks to conduct a synthetic climate risk analysis for Earth's forests in the 21st century. Despite large uncertainty in most regions we find that some forests are consistently at higher risk, including southern boreal forests and those in western North America and parts of the Amazon.

Climate Change Risks to Global Forest Health: Emergence of Unexpected Events of Elevated Tree Mortality Worldwide.

Recent observations of elevated tree mortality following climate extremes, like heat and drought, raise concerns about climate change risks to global forest health. We currently lack both sufficient data and understanding to identify whether these observations represent a global trend toward increasing tree mortality. Here, we document events of sudden and unexpected elevated tree mortality following heat and drought events in ecosystems that previously were considered tolerant or not at risk of exposure. These events underscore the fact that climate change may affect forests with unexpected force in the future. We use the events as examples to highlight current difficulties and challenges for realistically predicting such tree mortality events and the uncertainties about future forest condition. Advances in remote sensing technology and greater availably of high-resolution data, from both field assessments and satellites, are needed to improve both understanding and prediction of forest responses to future climate change.

Occurrence of crop pests and diseases has largely increased in China since 1970.

Crop pests and diseases (CPDs) are emerging threats to global food security, but trends in the occurrence of pests and diseases remain largely unknown due to the lack of observations for major crop producers. Here, on the basis of a unique historical dataset with more than 5,500 statistical records, we found an increased occurrence of CPDs in every province of China, with the national average rate of CPD occurrence increasing by a factor of four (from 53% to 218%) during 1970-2016. Historical climate change is responsible for more than one-fifth of the observed increment of CPD occurrence (22% ± 17%), ranging from 2% to 79% in different provinces. Among the climatic factors considered, warmer nighttime temperatures contribute most to the increasing occurrence of CPDs (11% ± 9%). Projections of future CPDs show that at the end of this century, climate change will lead to an increase in CPD occurrence by 243% ± 110% under a low-emissions scenario (SSP126) and 460% ± 213% under a high-emissions scenario (SSP585), with the magnitude largely dependent on the impacts of warmer nighttime temperatures and decreasing frost days. This observation-based evidence highlights the urgent need to accurately account for the increasing risk of CPDs in mitigating the impacts of climate change on food production.

Agricultural breadbaskets shift poleward given adaptive farmer behavior under climate change.

Modern food production is spatially concentrated in global "breadbaskets." A major unresolved question is whether these peak production regions will shift poleward as the climate warms, allowing some recovery of potential climate-related losses. While agricultural impacts studies to date have focused on currently cultivated land, the Global Gridded Crop Model Intercomparison Project (GGCMI) Phase 2 experiment allows us to assess changes in both yields and the location of peak productivity regions under warming. We examine crop responses under projected end of century warming using seven process-based models simulating five major crops (maize, rice, soybeans, and spring and winter wheat) with a variety of adaptation strategies. We find that in no-adaptation cases, when planting date and cultivar choices are held fixed, regions of peak production remain stationary and yield losses can be severe, since growing seasons contract strongly with warming. When adaptations in management practices are allowed (cultivars that retain growing season length under warming and modified planting dates), peak productivity zones shift poleward and yield losses are largely recovered. While most growing-zone shifts are ultimately limited by geography, breadbaskets studied here move poleward over 600 km on average by end of the century under RCP 8.5. These results suggest that agricultural impacts assessments can be strongly biased if restricted in spatial area or in the scope of adaptive behavior considered. Accurate evaluation of food security under climate change requires global modeling and careful treatment of adaptation strategies.

The heterogeneous effects of COVID-19 on Canadian household consumption, debt and savings.

This paper develops an agent-based model to quantify the impact of COVID-19 on household debt and savings. To build a representative cross-section of households that vary by income, debt portfolios and consumption baskets, we merge data from the Survey of Household Spending and the Survey of Financial Security. We construct paths for consumption and employment over the crisis, accounting for heterogeneous risk of unemployment across demographics, government transfers, and substitution between expenditure categories that vary in contact intensity. Our model simulations yield a heterogeneous effect of COVID-19 across the income distribution. Low-income households face the highest risk of unemployment, but transfers provide generous income replacement. Middle-income job losers see the fastest rise in debt because transfers only partially replace lost income. Most unplanned savings are accumulated by high-income households that face lower risk of unemployment and larger declines in hard-to-distance spending. We find the rise in savings could generate a brief jump of nearly 6% of monthly consumption.

Effets hétérogènes de la COVID­19 sur la consommation, la dette et l'épargne des foyers canadiens. Dans cet article, nous développons un modèle multiagent afin de chiffrer l'impact de la COVID­19 sur la dette et l'épargne des ménages. Compte tenu des disparités en matière de revenus, de portefeuilles de dettes ou de paniers de consommation, et afin d'obtenir un échantillonnage représentatif des foyers, nous avons fusionné les données de l'Enquête sur les dépenses des ménages et celles de l'Enquête sur la sécurité financière. Nous développons ensuite les trajectoires de la consommation et de l'emploi pendant la crise en tenant compte à la fois du risque hétérogène de chômage parmi les groupes démographiques, des transferts gouvernementaux et de la substitution parmi les catégories de dépenses nécessitant plus ou moins de distanciation physique. Les simulations de notre modèle montrent que la COVID­19 produit un effet hétérogène sur la distribution des revenus. Les ménages à faible revenu connaissent le plus grand risque de chômage, mais les transferts constituent de généreux revenus de remplacement. Les ménages à revenu moyen ayant subi des pertes d'emploi voient leur dette augmenter le plus rapidement, les transferts ayant seulement compensé une partie de leur baisse de revenus. Quant aux ménages à revenu élevé, ils ont accumulé la plus grande partie de l'épargne non planifiée, car ils sont moins susceptibles que les autres d'être touchés par des mises á pied et font face à des baisses de dépense plus importantes en matiére de biens et services restreints par la distanciation physique (hard­to­distance spending). Nous constatons que la hausse de l'épargne pourrait générer un bref regain de la consommation mensuelle de près de 6 %.

Large potential for crop production adaptation depends on available future varieties.

Climate change affects global agricultural production and threatens food security. Faster phenological development of crops due to climate warming is one of the main drivers for potential future yield reductions. To counter the effect of faster maturity, adapted varieties would require more heat units to regain the previous growing period length. In this study, we investigate the effects of variety adaptation on global caloric production under four different future climate change scenarios for maize, rice, soybean, and wheat. Thereby, we empirically identify areas that could require new varieties and areas where variety adaptation could be achieved by shifting existing varieties into new regions. The study uses an ensemble of seven global gridded crop models and five CMIP6 climate models. We found that 39% (SSP5-8.5) of global cropland could require new crop varieties to avoid yield loss from climate change by the end of the century. At low levels of warming (SSP1-2.6), 85% of currently cultivated land can draw from existing varieties to shift within an agro-ecological zone for adaptation. The assumptions on available varieties for adaptation have major impacts on the effectiveness of variety adaptation, which could more than half in SSP5-8.5. The results highlight that region-specific breeding efforts are required to allow for a successful adaptation to climate change.

Global irrigation contribution to wheat and maize yield.

Irrigation is the largest sector of human water use and an important option for increasing crop production and reducing drought impacts. However, the potential for irrigation to contribute to global crop yields remains uncertain. Here, we quantify this contribution for wheat and maize at global scale by developing a Bayesian framework integrating empirical estimates and gridded global crop models on new maps of the relative difference between attainable rainfed and irrigated yield (ΔY). At global scale, ΔY is 34 ± 9% for wheat and 22 ± 13% for maize, with large spatial differences driven more by patterns of precipitation than that of evaporative demand. Comparing irrigation demands with renewable water supply, we find 30-47% of contemporary rainfed agriculture of wheat and maize cannot achieve yield gap closure utilizing current river discharge, unless more water diversion projects are set in place, putting into question the potential of irrigation to mitigate climate change impacts.

Increasing climatic sensitivity of global grassland vegetation biomass and species diversity correlates with water availability.

Grasslands are key repositories of biodiversity and carbon storage and are heavily impacted by effects of global warming and changes in precipitation regimes. Patterns of grassland dynamics associated with variability in future climate conditions across spatiotemporal scales are yet to be adequately quantified. Here, we performed a global meta-analysis of year and growing season sensitivities of vegetation aboveground biomass (AGB), aboveground net primary productivity (ANPP), and species richness (SR) and diversity (Shannon index, H) to experimental climate warming and precipitation shifts. All four variables were sensitive to climate change. Their sensitivities to shifts in precipitation were correlated with local background water availability, such as mean annual precipitation (MAP) and aridity, and AGB and ANPP sensitivities were greater in dry habitats than in nonwater-limited habitats. There was no effect of duration of experiment (short vs long term) on sensitivities. Temporal trends in ANPP and SR sensitivity depended on local water availability; ANPP sensitivity to warming increased over time and SR sensitivity to irrigation decreased over time. Our results provide a global overview of the sensitivities of grassland function and diversity to climate change that will improve the understanding of ecological responses across spatiotemporal scales and inform policies for conservation in dry climates.

Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates.

We report large-scale estimates of Amazonian gap dynamics using a novel approach with large datasets of airborne light detection and ranging (lidar), including five multi-temporal and 610 single-date lidar datasets. Specifically, we (1) compared the fixed height and relative height methods for gap delineation and established a relationship between static and dynamic gaps (newly created gaps); (2) explored potential environmental/climate drivers explaining gap occurrence using generalized linear models; and (3) cross-related our findings to mortality estimates from 181 field plots. Our findings suggest that static gaps are significantly correlated to dynamic gaps and can inform about structural changes in the forest canopy. Moreover, the relative height outperformed the fixed height method for gap delineation. Well-defined and consistent spatial patterns of dynamic gaps were found over the Amazon, while also revealing the dynamics of areas never sampled in the field. The predominant pattern indicates 20-35% higher gap dynamics at the west and southeast than at the central-east and north. These estimates were notably consistent with field mortality patterns, but they showed 60% lower magnitude likely due to the predominant detection of the broken/uprooted mode of death. While topographic predictors did not explain gap occurrence, the water deficit, soil fertility, forest flooding and degradation were key drivers of gap variability at the regional scale. These findings highlight the importance of lidar in providing opportunities for large-scale gap dynamics and tree mortality monitoring over the Amazon.

Polar amplification of Pliocene climate by elevated trace gas radiative forcing.

Warm periods in Earth's history offer opportunities to understand the dynamics of the Earth system under conditions that are similar to those expected in the near future. The Middle Pliocene warm period (MPWP), from 3.3 to 3.0 My B.P, is the most recent time when atmospheric CO2 levels were as high as today. However, climate model simulations of the Pliocene underestimate high-latitude warming that has been reconstructed from fossil pollen samples and other geological archives. One possible reason for this is that enhanced non-CO2 trace gas radiative forcing during the Pliocene, including from methane (CH4), has not been included in modeling. We use a suite of terrestrial biogeochemistry models forced with MPWP climate model simulations from four different climate models to produce a comprehensive reconstruction of the MPWP CH4 cycle, including uncertainty. We simulate an atmospheric CH4 mixing ratio of 1,000 to 1,200 ppbv, which in combination with estimates of radiative forcing from N2O and O3, contributes a non-CO2 radiative forcing of 0.9 [Formula: see text] (range 0.6 to 1.1), which is 43% (range 36 to 56%) of the CO2 radiative forcing used in MPWP climate simulations. This additional forcing would cause a global surface temperature increase of 0.6 to 1.0 °C, with amplified changes at high latitudes, improving agreement with geological evidence of Middle Pliocene climate. We conclude that natural trace gas feedbacks are critical for interpreting climate warmth during the Pliocene and potentially many other warm phases of the Cenezoic. These results also imply that using Pliocene CO2 and temperature reconstructions alone may lead to overestimates of the fast or Charney climate sensitivity.

A Dynamic Model for Strategies and Dynamics of Plant Water-Potential Regulation Under Drought Conditions.

Vegetation responds to drought through a complex interplay of plant hydraulic mechanisms, posing challenges for model development and parameterization. We present a mathematical model that describes the dynamics of leaf water-potential over time while considering different strategies by which plant species regulate their water-potentials. The model has two parameters: the parameter λ describing the adjustment of the leaf water potential to changes in soil water potential, and the parameter Δψww describing the typical 'well-watered' leaf water potentials at non-stressed (near-zero) levels of soil water potential. Our model was tested and calibrated on 110 time-series datasets containing the leaf- and soil water potentials of 66 species under drought and non-drought conditions. Our model successfully reproduces the measured leaf water potentials over time based on three different regulation strategies under drought. We found that three parameter sets derived from the measurement data reproduced the dynamics of 53% of an drought dataset, and 52% of a control dataset [root mean square error (RMSE) < 0.5 MPa)]. We conclude that, instead of quantifying water-potential-regulation of different plant species by complex modeling approaches, a small set of parameters may be sufficient to describe the water potential regulation behavior for large-scale modeling. Thus, our approach paves the way for a parsimonious representation of the full spectrum of plant hydraulic responses to drought in dynamic vegetation models.

Pervasive shifts in forest dynamics in a changing world.

Forest dynamics arise from the interplay of environmental drivers and disturbances with the demographic processes of recruitment, growth, and mortality, subsequently driving biomass and species composition. However, forest disturbances and subsequent recovery are shifting with global changes in climate and land use, altering these dynamics. Changes in environmental drivers, land use, and disturbance regimes are forcing forests toward younger, shorter stands. Rising carbon dioxide, acclimation, adaptation, and migration can influence these impacts. Recent developments in Earth system models support increasingly realistic simulations of vegetation dynamics. In parallel, emerging remote sensing datasets promise qualitatively new and more abundant data on the underlying processes and consequences for vegetation structure. When combined, these advances hold promise for improving the scientific understanding of changes in vegetation demographics and disturbances.

Interactive climate factors restrict future increases in spring productivity of temperate and boreal trees.

Climate warming is currently advancing spring leaf-out of temperate and boreal trees, enhancing net primary productivity (NPP) of forests. However, it remains unclear whether this trend will continue, preventing for accurate projections of ecosystem functioning and climate feedbacks. Several ecophysiological mechanisms have been proposed to regulate the timing of leaf emergence in response to changing environmental cues, but the relative importance of those mechanisms remains unclear. Here, we use 727,401 direct phenological observations of common European forest trees to examine the dominant controls on leaf-out. Using the emerging mechanisms, we forecast future trajectories of spring arrival and evaluate the consequences for forest carbon dynamics. By representing hypothesized relationships with autumn temperature, winter chilling, and the timing of spring onset, we accurately predicted reductions in the advance of leaf-out. There was a strong consensus between our empirical model and existing process-based models, revealing that the advance in leaf-out will not exceed 2 weeks over the rest of the century. We further estimate that, under a 'business-as-usual' climate scenario, earlier spring arrival will enhance NPP of temperate and boreal forests by ~0.2 Gt per year at the end of the century. In contrast, previous estimates based on a simple degree-day model range around 0.8 Gt. As such, the expected NPP is drastically reduced in our updated model relative to previous estimates-by a total of ~25 Gt over the rest of the century. These findings reveal important environmental constraints on the productivity of broad-leaved deciduous trees and highlight that shifting spring phenology is unlikely to slow the rate of warming by offsetting anthropogenic carbon emissions.

Narrowing uncertainties in the effects of elevated CO2 on crops.

Plant responses to rising atmospheric carbon dioxide (CO2) concentrations, together with projected variations in temperature and precipitation will determine future agricultural production. Estimates of the impacts of climate change on agriculture provide essential information to design effective adaptation strategies, and develop sustainable food systems. Here, we review the current experimental evidence and crop models on the effects of elevated CO2 concentrations. Recent concerted efforts have narrowed the uncertainties in CO2-induced crop responses so that climate change impact simulations omitting CO2 can now be eliminated. To address remaining knowledge gaps and uncertainties in estimating the effects of elevated CO2 and climate change on crops, future research should expand experiments on more crop species under a wider range of growing conditions, improve the representation of responses to climate extremes in crop models, and simulate additional crop physiological processes related to nutritional quality.

Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble.

Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.